Mining product reputations on the Web
Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining
Mining the peanut gallery: opinion extraction and semantic classification of product reviews
WWW '03 Proceedings of the 12th international conference on World Wide Web
Sentiment Mining in WebFountain
ICDE '05 Proceedings of the 21st International Conference on Data Engineering
Thumbs up or thumbs down?: semantic orientation applied to unsupervised classification of reviews
ACL '02 Proceedings of the 40th Annual Meeting on Association for Computational Linguistics
Computational Linguistics
ACL '04 Proceedings of the 42nd Annual Meeting on Association for Computational Linguistics
Extracting semantic orientations of words using spin model
ACL '05 Proceedings of the 43rd Annual Meeting on Association for Computational Linguistics
Consumer behavior analysis from buzz marketing sites over time series concept graphs
KES'11 Proceedings of the 15th international conference on Knowledge-based and intelligent information and engineering systems - Volume Part II
Social media analysis determining the number of topic clusters from buzz marketing site
International Journal of Computational Science and Engineering
Detecting unexpected correlation between a current topic and products from buzz marketing sites
DNIS'11 Proceedings of the 7th international conference on Databases in Networked Information Systems
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Social computing services, which enable people to easily communicate and effectively share the information through the Web, have rapidly spread recently. In the marketing research domain, buzz marketing sites as social computing services have become important in recognizing the reputation of products hold with users. This paper proposes a reputation analysis framework for the buzz marketing sites. Our framework consists of four steps: the first is to extract the topics of the product using natural language processing. The input data comprises consumer messages on buzz marketing sites. Next, important topics on the products are extracted. The third step is to detect emerging consumer needs by identifying new burst topics. Finally, the results are visualized. Based on our framework, product characteristics and emerging consumer needs are extracted and reputations are visualized.